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Updated: May 13, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Published on: December 15, 2023

Enhancing multi-class neurodegenerative disease classification using deep learning and explainable local

Jamel Baili¹, Abdullah Alqahtani², Ahmad Almadhor³

¹Department of Computer Engineering, College of Computer Science, King Khalid University, Abha, Saudi Arabia.

Frontiers in Medicine

|April 16, 2025

Summary

This summary is machine-generated.

This study introduces novel deep learning models, Residual-based Attention Convolutional Neural Network (RbACNN) and Inverted Residual-based Attention Convolutional Neural Network (IRbACNN), for diagnosing Alzheimer's disease and Parkinson's disease with 99.92% accuracy.

Keywords:

Alzheimer's disease (AD)Parkinson's disease (PD)deep learning models medical image analysis neurodegenerative disorders

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Alzheimer's disease (AD) and Parkinson's disease (PD) are leading neurodegenerative disorders.
Accurate and early diagnosis is crucial for effective management and reducing global health burdens.

Purpose of the Study:

To introduce two novel deep learning architectures, RbACNN and IRbACNN, for enhanced medical image classification.
To improve the accuracy and interpretability of automated diagnoses for AD and PD.

Main Methods:

Developed Residual-based Attention Convolutional Neural Network (RbACNN) and Inverted Residual-based Attention Convolutional Neural Network (IRbACNN).
Integrated self-attention mechanisms for improved feature extraction and explainable AI (XAI) for transparency.
Applied preprocessing techniques including histogram equalization and batch creation.

Main Results:

The proposed RbACNN and IRbACNN models achieved an outstanding classification accuracy of 99.92%.
The models demonstrated enhanced feature extraction and interpretability through attention mechanisms.

Conclusions:

The developed deep learning architectures combined with XAI facilitate early and precise diagnosis of AD and PD.
These advancements hold significant potential for reducing the global impact of neurodegenerative diseases.